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Time‐dependent biomarker accuracy in forecasting mild cognitive impairment
Jonathan Gallego Rudolf, Alex I. Wiesman, Sylvain Baillet, Sylvia Villeneuve. Alzheimer s & Dementia, 2025-12-01
Topic: Risk, prevention and care in population studies
Neurophysiological activity features and tau pet provided additional information to the clinical model when acquired up to ∼4 years prior to diagnosis, but their accuracy decreased at larger intervals. In contrast, the accuracy gained by incorporating Aβ PET or plasma biomarkers remained high up to 6 years before diagnosis (Figure 2). These observations were confirmed after running stepwise logistic regression on the model including all biomarkers, highlighting the contribution of age, sex, plasma p-tau217, Aβ and tau PET and neurophysiological activity for predicting MCI progression at different time intervals (Figure 3). Overall, our results delineate a timeline of the accuracy provided by different biomarkers for predicting progression to MCI. Such findings highlight the dynamic sensitivity of different biomarkers, which is dependent on the time lapse between biomarkers collection and clinical diagnosis. This is particularly relevant for future clinical trials that intend to use biomarkers for screening participants.
Key findings
- Neurophysiological activity features and tau pet provided additional information to the clinical model when acquired up to ∼4 years prior to diagnosis, but their accuracy decreased at larger intervals.
- In contrast, the accuracy gained by incorporating Aβ PET or plasma biomarkers remained high up to 6 years before diagnosis (Figure 2).
- These observations were confirmed after running stepwise logistic regression on the model including all biomarkers, highlighting the contribution of age, sex, plasma p-tau217, Aβ and tau PET and neurophysiological activity for predicting MCI progression at different time intervals (Figure 3).
- Overall, our results delineate a timeline of the accuracy provided by different biomarkers for predicting progression to MCI.
- Such findings highlight the dynamic sensitivity of different biomarkers, which is dependent on the time lapse between biomarkers collection and clinical diagnosis.
Why it matters
This study found that the accuracy of different biomarkers for predicting progression to mild cognitive impairment depends on how long before diagnosis they are measured, with some remaining accurate up to six years prior. It is about biomarker timing for prediction and does not test any treatment, cure, or prevention.
Study details
- Study designlongitudinal study
- Participants102
- Evidence strengthModerate
How this study scored 48/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer s & Dementia) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 2 / 4 | 102 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 1 / 6 | Cited 0.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Sylvia Villeneuve: h-index 48 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | McGill University: institution h-index 1,373 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41433380) |
| Free to read | 2 / 2 | Free to read |
| Total | 48 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1002/alz70862_110285
- PubMed: 41433380
- Original source →
- Free full text: Alzheimer s & Dementia →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Rudolf, J. G., Wiesman, A. I., Baillet, S., & Villeneuve, S. (2025). Time‐dependent biomarker accuracy in forecasting mild cognitive impairment. Alzheimer s & Dementia. https://doi.org/10.1002/alz70862_110285
Vancouver
Rudolf JG, Wiesman AI, Baillet S, Villeneuve S. Time‐dependent biomarker accuracy in forecasting mild cognitive impairment. Alzheimer s & Dementia. 2025. doi:10.1002/alz70862_110285. PMID: 41433380.
BibTeX
@article{rudolf2025-41433380,
author = {Rudolf, Jonathan Gallego and Wiesman, Alex I. and Baillet, Sylvain and Villeneuve, Sylvia},
title = {{Time‐dependent biomarker accuracy in forecasting mild cognitive impairment}},
journal = {Alzheimer s \& Dementia},
year = {2025},
doi = {10.1002/alz70862_110285},
pmid = {41433380},
url = {https://doi.org/10.1002/alz70862_110285}
}
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